For training the first GCN layer of BCNet, since partial occlusion cases only occupy a small fraction compared to the complete objects in COCO, we filter out part of the non-occluded ROI proposals to keep occlusion cases taking up 50% for balance sampling. SGD with momentum is employed for training 90K iterations which starts with 1K constant warm-up iterations. The batch size is set to 16 and initial learning rate is 0.01. In ablation study, ResNet-50-FPN [1]} is used as backbone and the input images are resized without changing the aspect ratio by keeping the shorter side and longer side of no more than 600 and 900 pixels respectively. For leaderboard comparison, we adopt the scale-jitter where the shorter image side is randomly sampled from [640, 800] following [2]}, [3]}, [4]}.
